(+)-JRT, a de novo synthesized LSD analogue (two-atom transposition), retains potent neuroplasticity-promoting effects while markedly reducing hallucinogenic liability. It offers a path toward treating depression, addiction, and cognition deficits in patients (e.g., SCZ, psychosis family history) for whom classical psychedelics are contraindicated.
- **Mechanism:** promotes cortical spinogenesis / dendritic growth (same pathway as LSD)
- **Selectivity:** improved receptor pharmacology; does *not* amplify psychosis-associated behavioral or gene-expression signatures
- **Behavioral readouts:** antidepressant- and cognition-relevant assay effects
- **Design principle:** minimal structural edit → "nonhallucinogenic psychoplastogen" class
- **Mechanism:** promotes cortical spinogenesis / dendritic growth (same pathway as LSD)
- **Selectivity:** improved receptor pharmacology; does *not* amplify psychosis-associated behavioral or gene-expression signatures
- **Behavioral readouts:** antidepressant- and cognition-relevant assay effects
- **Design principle:** minimal structural edit → "nonhallucinogenic psychoplastogen" class
Simon Batt writes about Fugleramme, a Raspberry Pi 5 project by arnegiacomo that passively listens to bird calls via a USB microphone and displays hand-curated 1800s natural-history cutouts of the species it detects on an Inky e-ink display. The system runs fully offline on the Pi using BirdNET-Go for detection, deliberately avoiding generated images in favor of genuine historical illustrations.
- The mic is a lavalier with a thin cable taped under the roof and threaded in through the window
- Currently features European birds; US species are in progress
- A live demo of the creator's own device is available at their dev website
- Open-source on GitHub under the name Fugleramme
- The mic is a lavalier with a thin cable taped under the roof and threaded in through the window
- Currently features European birds; US species are in progress
- A live demo of the creator's own device is available at their dev website
- Open-source on GitHub under the name Fugleramme
bex is an open-source, self-hostable PaaS that positions itself as an AI-native alternative to Render, letting developers push Git and receive a deployed URL on their own Kubernetes infrastructure. Coding agents operate as first-class users via MCP alongside the dashboard, CLI, REST, and GraphQL interfaces, all backed by a shared Go core. The platform uses a Kubernetes operator with Cluster API for machine provisioning, supports Render-style `render.yaml` Blueprints for declarative service definitions, and ships managed Postgres, Key Value, logs, metrics, autoscaling, custom domains with TLS, and SSH access.
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
md-mode is an Emacs major mode that provides Org-inspired structural editing for Markdown files with live font-lock styling in the edit view and a read-only rendered view in the same buffer. It requires only Emacs 29.1 or newer and no external Markdown processor for its core in-buffer rendering.
- Tables remain valid Markdown source while being rendered responsively with width-aware column layout, debounced resize relayout, and per-buffer measurement caching
- Optional local integrations render LaTeX math, Mermaid, PlantUML, and Graphviz diagrams asynchronously with no content sent to remote services
- The renderer was adapted from agent-shell-markdown.el by Alvaro Ramirez
- Tables remain valid Markdown source while being rendered responsively with width-aware column layout, debounced resize relayout, and per-buffer measurement caching
- Optional local integrations render LaTeX math, Mermaid, PlantUML, and Graphviz diagrams asynchronously with no content sent to remote services
- The renderer was adapted from agent-shell-markdown.el by Alvaro Ramirez
mini-swe-agent is a radically simple Python-based agent from the Princeton and Stanford team behind SWE-bench that uses only bash as its tool, maintains a completely linear message history, and executes each action via independent subprocess.run calls. Despite being roughly 100 lines of core agent code, it scores over 74% on SWE-bench verified and is used by organizations including Meta, NVIDIA, IBM, and Anyscale.
- The core design argument is that as language models grow more capable, elaborate tool scaffolds become unnecessary and the LM itself should drive the shell
- Supports sandboxed deployment via docker, podman, singularity, bwrap, and others; installable from PyPI via uvx, pipx, or pip
- The core design argument is that as language models grow more capable, elaborate tool scaffolds become unnecessary and the LM itself should drive the shell
- Supports sandboxed deployment via docker, podman, singularity, bwrap, and others; installable from PyPI via uvx, pipx, or pip
Amanda Caswell writes about Claude Fable 5.1, Anthropic's latest high-end model, and proposes a single prompt designed to showcase its agentic capabilities. The prompt asks the model to critique an existing project the user is already working on, identify what's wrong with their thinking, and then refactor or redesign the whole thing while explaining its reasoning at each decision point. Caswell tested it by uploading her entire book manuscript without giving any hints about what she thought was wrong, and found the model preserved her voice while surfacing issues she had missed.
- Features a 1-million-token context window, suited for large codebases and lengthy documents
- Anthropic claims reduced costs on agentic workloads through cheaper caching
- Caswell notes that LLM explanations are justifications, not literal transcripts of internal reasoning
- She contrasts Claude's more questioning approach with ChatGPT and Gemini, which she says tend to execute edits without challenging assumptions
>": I'm building describe a real project you're actually working on, in detail » . Here's my current approach: paste your code, outline, or plan » . Tell me what's wrong with my thinking, what I'm missing, and then refactor or redesign the whole thing — but explain your reasoning at each decision point so I can learn from it."
- Features a 1-million-token context window, suited for large codebases and lengthy documents
- Anthropic claims reduced costs on agentic workloads through cheaper caching
- Caswell notes that LLM explanations are justifications, not literal transcripts of internal reasoning
- She contrasts Claude's more questioning approach with ChatGPT and Gemini, which she says tend to execute edits without challenging assumptions
>": I'm building describe a real project you're actually working on, in detail » . Here's my current approach: paste your code, outline, or plan » . Tell me what's wrong with my thinking, what I'm missing, and then refactor or redesign the whole thing — but explain your reasoning at each decision point so I can learn from it."
Santosh Mahale writes that most teams default to vector-database RAG without evaluating whether it fits their data and query patterns, when the retrieval architecture is the primary lever for production success. He compares three options—Traditional (semantic search via embeddings), Vectorless (exact lookups via SQL, BM25, APIs, or graph traversal with no vector store), and Hybrid (both retrieval paths merged and reranked)—and recommends starting with the simplest approach that solves the use case, measuring where it fails, then adding complexity only where data demands it.
- Most RAG failures are retrieval failures (wrong context reaching the LLM), not model failures
- Vectorless RAG is underused; structured-data workloads like log analysis, K8s event lookups, and compliance records often outperform Traditional RAG with far less infrastructure
- Hybrid RAG is the eventual landing spot for most enterprise deployments but adds two retrieval paths, a merge step, and a reranker to maintain
- The article positions RAG variants within a broader stack: LLM → RAG architectures → agents → MCP → agentic systems, each solving a different layer
- Most RAG failures are retrieval failures (wrong context reaching the LLM), not model failures
- Vectorless RAG is underused; structured-data workloads like log analysis, K8s event lookups, and compliance records often outperform Traditional RAG with far less infrastructure
- Hybrid RAG is the eventual landing spot for most enterprise deployments but adds two retrieval paths, a merge step, and a reranker to maintain
- The article positions RAG variants within a broader stack: LLM → RAG architectures → agents → MCP → agentic systems, each solving a different layer
Jo Hinchliffe writes a tutorial for building a self-hosted personal knowledge management system inspired by Tiago Forte's *Building a Second Brain*. The setup pairs Logseq, a privacy-focused open-source note-taking app that stores everything as local Markdown files with a powerful tagging system, with Syncthing, a peer-to-peer file synchronisation tool that keeps the note database in sync across multiple Raspberry Pi devices without passing data through any cloud server.
- Syncthing is not a backup: deletions propagate to all linked devices, so a separate cron-driven rsync script is recommended for periodic backups
- Each tag in Logseq automatically becomes a page, creating a navigable graph of linked information
- Logseq currently installs only via Snap on Raspberry Pi OS
- The system was demonstrated on a Raspberry Pi 500, a Pi Zero, and a Uconsole (CM4)
- Logseq supports embedding links, videos, and even PDF annotation with tagging of specific pages
- Syncthing is not a backup: deletions propagate to all linked devices, so a separate cron-driven rsync script is recommended for periodic backups
- Each tag in Logseq automatically becomes a page, creating a navigable graph of linked information
- Logseq currently installs only via Snap on Raspberry Pi OS
- The system was demonstrated on a Raspberry Pi 500, a Pi Zero, and a Uconsole (CM4)
- Logseq supports embedding links, videos, and even PDF annotation with tagging of specific pages
Ayush Pande writes about how his Proxmox server became more useful after he stopped treating LXCs as Docker containers. Coming from a Docker-only background, he initially tried to manage LXCs as ephemeral, single-app environments, but realized they are fundamentally system containers that run a full userland (systemd, SSH, cron) and require managing the underlying OS just like a lightweight VM. Once he shifted his approach, his home lab's utility improved significantly.
- LXCs are system containers running a full userland, unlike Docker's single-process application containers
- Proxmox developers do not recommend Docker-in-LXC; live migration can break nested container environments
- VMs are the preferred Docker hosts from a security standpoint
- On low-spec hardware (e.g., a decade-old laptop), Docker-in-LXC can still make the box usable for experimental services
- LXCs are system containers running a full userland, unlike Docker's single-process application containers
- Proxmox developers do not recommend Docker-in-LXC; live migration can break nested container environments
- VMs are the preferred Docker hosts from a security standpoint
- On low-spec hardware (e.g., a decade-old laptop), Docker-in-LXC can still make the box usable for experimental services
Jonas Kaninda writes that Posta is a self-hosted, developer-first email platform handling outbound, inbound, and relayed email through a single HTTP API, with SMTP available where needed. It positions itself as a fully self-hostable alternative to SendGrid, Mailgun, and Postmark, giving teams ownership of their email infrastructure, data, and deliverability.
- Ships as a single Go binary in two modes (server and worker); all state lives in PostgreSQL, Redis, and optional S3-compatible object storage.
- Includes a Vue 3 dashboard, web form ingest with spam scoring, A/B campaign testing, RFC 8058 one-click unsubscribe, and Prometheus metrics.- Official SDKs for Go, PHP, Java, and .NET; also deployable as a managed template on Miabi.
- Requires Go 1.27+, PostgreSQL, and Redis
- Ships as a single Go binary in two modes (server and worker); all state lives in PostgreSQL, Redis, and optional S3-compatible object storage.
- Includes a Vue 3 dashboard, web form ingest with spam scoring, A/B campaign testing, RFC 8058 one-click unsubscribe, and Prometheus metrics.- Official SDKs for Go, PHP, Java, and .NET; also deployable as a managed template on Miabi.
- Requires Go 1.27+, PostgreSQL, and Redis
